Supply chain companies are constantly facing the changing customer demands while having constant pressure to reduce costs and improve margins. Given the current situation in shipping with congestion, the deficiencies of equipment (containers) for the consignments and the COVID-19 pandemic affecting many businesses simultaneously all over the world, Supply Chain Forecasting has gained more significance in recent times than ever.
What is Supply Chain Forecasting?
Supply Chain Forecasting (as the name suggests) is the concept of predicting future or potential events within a supply chain. It relies on the premise of analytical data which can then be statistically extrapolated to draw a predictive timeline. By collating competitor data, intelligence and identifying patterns, AI & ML based algorithms can fulfill this cause seamlessly.
Why is supply chain forecasting so significant?
Forecasting in supply chain management is important as it enables both suppliers and consumers to collaborate efficiently, ensuring a seamless flow of goods or services. It essentially aligns supply and demand by using historical data trends to draw future conclusions from past events. This also acts as a safeguard against potential mishaps, such as inaptly managed demand induced supply gaps or handling unexpected interruptions within the chain itself. Such ‘insurance’ can always foster greater confidence & trust within supply margins to ensure that supply always remains unaffected or at least as good as can be. So without further ado, let’s explore what are the top 7 ways to successfully forecast supply chains. However, before we do so, we should understand the concept of demand sensing.
Demand sensing – always detecting live consumer needs
The idea of responsive supply and demand matching depends on the ability to quickly detect a change in demand – and then flag it. Consequently, acting upon such information or updates is merely a matter of how quickly systems are programmed to initiate or trigger a remedial response. Hence, unification of suchlike is extremely important, if not essential to remain successful in this competitive market world. Such tight margin environments scale various industries and can be quite ruthless towards those brands who fail to perceive an issue in time upon its emergence. Makes sense, right? So now finally on to the 7 ways to successfully forecast supply chains. Here comes supply chain demand forecasting!
7 ways to successfully forecast supply chains
- Probabilistic forecasting which eliminates uncertainty and delivers accurate plus dependable results. These are based on data intelligence derived from previous trends, however with the exception of recreating multiple permutations to sample an outcome. This is how probabilistic forecasting manages to be much more precise, as it focuses on mimicking expected events and recreating entire situational settings. Now we can better bet on selective outcomes using this innovative and apt model, rather than conventional guesswork. Never assume when there’s room to predict the future so soon! Supply chain forecasting softwareto the rescue here!
- Machine Learning also drives digital transformation for supply chain forecasting. Imagine planning an event but being mindful of risk factors – including seasonal or economic trend changes or otherwise. The point is to avoid errors which can incur losses and rather focus on what can be done to either minimize these or mitigate them entirely. Don’t forget that this can also be integrated with other similar and augmenting processes, such as AI which supplement its operational ability. These can also help to automate such lengthy processes to not only save time but also reinvest such salvaged resources for further development. Now patterns can be identified, replicated and run.
- Intermittent demand calls for the need for master supply contracts which have varied shifting rates. Hence, slower chains can be handled according to planned demand, whilst faster ones can be adapted to on a live basis. Now customer satisfaction via greater loyalty & retention rates, plus upholding a brand image are just some benefits of this. Moreover, omnichannel resource assignment is enhanced, be it in the on or offline mode. Costs can also be reduced, as can the rate of obsolescence, especially with fashion & technology by only manufacturing what’s actually required, rather than what may be expected (and yet still not occurring as so). Even planning productivity can be boosted, whilst waste is reduced. With better sales performance and less lost sales revenue.
- Granular visibility yields better insights, whilst decision grade analytics make informed decisions on your behalf. Greater evaluation of inventory management also ensures that such data is handled as aptly as possible. This yields fidelity data which can be fed into an agile system to adapt to customer preferences in real-time. Now scale at speed to respond to unprecedented demand within seconds! Unifying demand signals along with a common understanding of internal communication enables utter unification of all pertinent processes to drive supply chain forecasting.
- Customer collaboration and forging supplier partnerships can both provide much needed intel, as well as unveil vital pertinent information about supply chains. This can then help to forecast the required details with more guided relevance. Pure forecasting and Material Resource Planning both play vital roles in knowing what to do, when, where, how & of course, why. Demand planning training can equip and empower workers to operate software required to both capture and then interpret vital data. Consequently, supply chains can be forecasted even better with more predictable results, which are easier to discern.
- Maintaining awareness by being and remaining cognizant as well as vigilant at all times regarding supply chain trends will always help to handle any sudden changes which may otherwise impact your business. Planning as far ahead as possible is always recommended, as is the practice of rationing resources in case of demand surges and consequential supply shortfalls. Equally, supply reductions also require apt handling to ensure fluid workflows, without which, chaos can quite easily and literally ensue. Hence it’s always better to plan and execute, rather than blindly proceeding.
- Finally, maintaining direct contact with consumer endpoints can also give valuable insights into their actual expectations, so that these can all be fulfilled. Now everyone can gain valuable insights from this and all the aforementioned points to understand where their role in the supply chain is and its future status (based on expected results, as per previous trends). Communication is always key; however it must be effective to actually exert any gaugable impact. A mixture of purpose-built modules to monitor the progress made and predict what could happen, is undoubtedly the way forward.
Summary – supply chain forecasting: how to make it a success?
A mix of carefully applied statistical probability, data trend analysis, effective communication, machine learning algos and AI driven pathways can collectively achieve decent supply chain forecasting results. The other point to consider is that whenever suppliers look to vary their production, they should always know who their consumers are. It’s all well and good that interconnect systems facilitate the process, however this is pointless when we’re obvious who the customers really are, what exactly they want (product/service & quantity) and when or where.